/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/

#include <fstream>

#include "../src/io.h"
#include "common/log.h"
#include "framework/framework.pb.h"
#include "framework/lod_tensor.h"
#include "framework/program_desc.h"
#include "framework/scope.h"
#include "framework/tensor.h"
#define PADDLE_MOBILE_DEBUG 1
namespace paddle_mobile {

void ReadBinaryFile(const std::string &filename, std::string *contents) {
  std::ifstream fin(filename, std::ios::in | std::ios::binary);
  fin.seekg(0, std::ios::end);
  contents->clear();
  contents->resize(fin.tellg());
  fin.seekg(0, std::ios::beg);
  fin.read(&(contents->at(0)), contents->size());
  fin.close();
}

template <typename Dtype, Precision P>
void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
                               const std::string &file_path) {
  std::ifstream is(file_path);

  std::fpos<mbstate_t> pos;
  pos = is.tellg();  // save   current   position
  is.seekg(0, std::ios::end);
  is.seekg(pos);  // restore   saved   position

  // 1. version
  uint32_t version;
  is.read(reinterpret_cast<char *>(&version), sizeof(version));

  // 2 Lod information
  uint64_t lod_level;
  is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
  auto &lod = *tensor->mutable_lod();
  lod.resize(lod_level);
  for (uint64_t i = 0; i < lod_level; ++i) {
    uint64_t size;
    is.read(reinterpret_cast<char *>(&size), sizeof(size));
    std::vector<size_t> tmp(size / sizeof(size_t));
    is.read(reinterpret_cast<char *>(tmp.data()),
            static_cast<std::streamsize>(size));
    for (auto j : tmp) {
      LOG(kLOG_DEBUG1) << "    lod - " << j;
    }
    lod[i] = tmp;
  }

  // 3. tensor version
  uint32_t tensor_version;
  is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));

  // 4. tensor desc
  int32_t size;
  is.read(reinterpret_cast<char *>(&size), sizeof(size));
  std::unique_ptr<char[]> buf(new char[size]);
  is.read(reinterpret_cast<char *>(buf.get()), size);

  framework::proto::VarType::TensorDesc desc;
  desc.ParseFromArray(buf.get(), size);

  int memory_size = 1;
  for (auto l : desc.dims()) {
    memory_size *= l;
  }

  std::vector<int64_t> dims;
  dims.reserve(static_cast<size_t>(desc.dims().size()));
  std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
  tensor->Resize(framework::make_ddim(dims));

  void *memory = tensor;
  int type_size = 0;
  switch (desc.data_type()) {
    case framework::proto::VarType::FP16:
      type_size = 2;
      break;
    case framework::proto::VarType::FP32:
      type_size = 4;
      memory = tensor->mutable_data<float>();
      break;
    case framework::proto::VarType::FP64:
      type_size = 8;
      break;
    case framework::proto::VarType::INT32:
      type_size = 4;
      break;
    case framework::proto::VarType::INT64:
      type_size = 8;
      break;
    case framework::proto::VarType::BOOL:
      type_size = 1;
      break;
    default:
      break;
  }

  is.read(static_cast<char *>(memory), memory_size * type_size);
  is.close();
}

template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
    const std::string &dirname) {
  std::string model_filename = dirname + "/__model__";
  std::string program_desc_str;
  ReadBinaryFile(model_filename, &program_desc_str);
  framework::proto::ProgramDesc program_desc_proto;
  program_desc_proto.ParseFromString(program_desc_str);

  std::shared_ptr<framework::ProgramDesc> originProgramDesc =
      std::make_shared<framework::ProgramDesc>(program_desc_proto);

  framework::Program<Dtype, P> program;
  program.originProgram = originProgramDesc;

  std::shared_ptr<framework::Scope> scope =
      std::make_shared<framework::Scope>();
  program.scope = scope;

  originProgramDesc->Block(0);

  for (const auto &block : originProgramDesc->Blocks()) {
    for (int i = 0; i < block->Vars().size(); ++i) {
      std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
      auto var = scope->Var(var_desc->Name());
      if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
        if (var_desc->Persistable() &&
            var_desc->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
            var_desc->GetType() != framework::proto::VarType::FETCH_LIST) {
          auto tensor = var->GetMutable<framework::LoDTensor>();
          // to load
          LoadVar(tensor, dirname + "/" + var_desc->Name());
        }
      } else {
        //  TODO by someone
      }
    }
  }

#ifdef PADDLE_MOBILE_DEBUG
  for (const auto &block : program_desc_proto.blocks()) {
    LOG(kLOG_DEBUG) << "block: " << block.idx();
    for (int j = 0; j < block.ops().size(); ++j) {
      //      if (j == 2) {
      //        break;
      //      }
      framework::proto::OpDesc op = block.ops()[j];
      LOG(kLOG_DEBUG1) << "op: " << op.type();
      for (int m = 0; m < op.inputs_size(); ++m) {
        const framework::proto::OpDesc::Var &var = op.inputs(m);
        LOG(kLOG_DEBUG2) << "input parameter: " << var.parameter();
        for (const auto &n : var.arguments()) {
          LOG(kLOG_DEBUG3) << "argument - " << n;
        }
      }

      for (int y = 0; y < op.outputs_size(); ++y) {
        const framework::proto::OpDesc::Var &var = op.outputs(y);
        LOG(kLOG_DEBUG2) << "out parameter: " << var.parameter();
        for (const auto &z : var.arguments()) {
          LOG(kLOG_DEBUG3) << "argument - " << z;
        }
      }

      for (const auto &attr : op.attrs()) {
        LOG(kLOG_DEBUG2) << "attr name: " << attr.name();

        switch (attr.type()) {
          case framework::proto::AttrType::BOOLEAN:
            LOG(kLOG_DEBUG3) << "boolen: " << attr.b();
            break;
          case framework::proto::AttrType::INT:
            LOG(kLOG_DEBUG3) << "int: " << attr.i();
            break;
          case framework::proto::AttrType::FLOAT:
            LOG(kLOG_DEBUG3) << "float: " << attr.f();
          case framework::proto::AttrType::STRING:
            LOG(kLOG_DEBUG3) << "string: " << attr.s();
          case framework::proto::AttrType::BOOLEANS:
            for (int y = 0; y < attr.bools_size(); ++y) {
              LOG(kLOG_DEBUG3) << "bools: " << attr.bools(y);
            }
          case framework::proto::AttrType::LONG:
            LOG(kLOG_DEBUG3) << "long: " << attr.l();
          case framework::proto::AttrType::FLOATS:
            for (int y = 0; y < attr.floats_size(); ++y) {
              LOG(kLOG_DEBUG3) << "floats: " << attr.floats(y);
            }
          case framework::proto::AttrType::INTS:
            for (int y = 0; y < attr.ints_size(); ++y) {
              LOG(kLOG_DEBUG3) << "ints: " << attr.ints(y);
            }
          case framework::proto::AttrType::STRINGS:
            for (int y = 0; y < attr.strings_size(); ++y) {
              LOG(kLOG_DEBUG3) << "strings: " << attr.strings(y);
            }
          case framework::proto::BLOCK:
            break;
        }
      }
    }

    for (const auto &var : block.vars()) {
      if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
        LOG(kLOG_DEBUG1) << "var name: " << var.name();
        const framework::proto::VarType::TensorDesc &tensor_desc =
            var.type().lod_tensor().tensor();
        LOG(kLOG_DEBUG2) << "in var tensor desc dims size: "
                         << tensor_desc.dims().size();
        for (int l = 0; l < tensor_desc.dims().size(); ++l) {
          LOG(kLOG_DEBUG3) << "var tensor desc dim " << l
                           << " value: " << tensor_desc.dims()[l];
        }
      }

      if (var.persistable() &&
          var.type().type() != framework::proto::VarType::FEED_MINIBATCH &&
          var.type().type() != framework::proto::VarType::FETCH_LIST) {
        std::string file_path = dirname + "/" + var.name();
        std::ifstream is(file_path);
        std::fpos<mbstate_t> pos;
        pos = is.tellg();  // save   current   position
        is.seekg(0, std::ios::end);
        is.seekg(pos);  // restore   saved   position

        // 1. version
        uint32_t version;
        is.read(reinterpret_cast<char *>(&version), sizeof(version));

        // 2 Lod information
        uint64_t lod_level;
        is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
        for (uint64_t i = 0; i < lod_level; ++i) {
          uint64_t size;
          is.read(reinterpret_cast<char *>(&size), sizeof(size));
          std::vector<size_t> tmp(size / sizeof(size_t));
          is.read(reinterpret_cast<char *>(tmp.data()),
                  static_cast<std::streamsize>(size));
          for (int j = 0; j < tmp.size(); ++j) {
          }
        }

        is.read(reinterpret_cast<char *>(&version), sizeof(version));

        int32_t size;
        is.read(reinterpret_cast<char *>(&size), sizeof(size));
        std::unique_ptr<char[]> buf(new char[size]);
        is.read(reinterpret_cast<char *>(buf.get()), size);

        framework::proto::VarType::TensorDesc desc;
        desc.ParseFromArray(buf.get(), size);

        int memory_size = 1;
        for (long long l : desc.dims()) {
          memory_size *= l;
        }

        int type_size = 0;
        switch (desc.data_type()) {
          case framework::proto::VarType::FP16:
            type_size = 2;
            break;
          case framework::proto::VarType::FP32:
            type_size = 4;
            break;
          case framework::proto::VarType::FP64:
            type_size = 8;
            break;
          case framework::proto::VarType::INT32:
            type_size = 4;
            break;
          case framework::proto::VarType::INT64:
            type_size = 8;
            break;
          case framework::proto::VarType::BOOL:
            type_size = 1;
            break;
          default:
            break;
        }

        void *memory = malloc(memory_size * type_size);
        is.read(static_cast<char *>(memory), memory_size * type_size);
        is.close();
      } else {
        //  TODO
      }
    }
  }

#endif
  return program;
}

template class Loader<CPU, Precision::FP32>;

}  // namespace paddle_mobile